\(\diamond \)Approaches: PointNet has revolutionized the process of classifying and segmenting 3D pointcloud datasets [1]}. There are three appealing properties of this approach: unordered input, local and global feature extraction, and invariance under transformations. Through a local network, i.e. T-net, spatial information has been transformed into local and global features. In contrast to PointNet, DGCNN [2]} incorporates structural information by computing the pairwise distance using k-nearest neighbors(KNN) of points.
